There are a number of approaches to simulation depending on the purpose to which the
simulation is intended, the models available, and the information available. However, the
general form of a simulation is fairly regular. Simulations are almost always used to
validate, explore or extrapolate the properties of a model of some system. The basis of
simulation is modelling the behaviour of a model, and thus it is possible to have a very
simple simulation consisting of a single model operation applied to a single model object
(or structure) . However, a simulation is usually a model of a system consisting of at least
two sub-models. which interact either by one being directly influenced by the other, direct
mutual influence, or where both operate on at least one common object (Figure 2).

One general motivation for simulation is that we may lack the means to substantively
describe or characterise the interaction of these sub-models using either prose or formal
devices, but we can observe their interaction case by case. Simulation is then suitable for
those situations which are beyond our normal means to model in a concrete fashion. It is
notable that simulations are widely used in applications of the physical sciences in
engineering, for although these disciplines have very good models for describing the local
interaction of variables, they too, in general, lack the means to describe or predict the
interactions that a complex system might have. Thus airplanes are not built on the basis of
first principles of physics, but are designed using these principles, and the designs are
refined, first using computer simulations, and then physical simulations such as a wind
tunnel.

There are two basic types of simulation components, stochastic and deterministic, which
roughly correspond to Levi-Strauss's distinction between statistical and mechanical models
(Levi-Strauss 1965).

Stochastic simulations generally have a probabilistic component, though not usually
unconditioned probabilities (eg sampling from the normal distribution, poisson distribution
or even catastrophe spaces where the probability density changes with different paths or
other spaces, for that matter). Stochastic simulations generally must be performed a
number of times until there is an adequate sample of results to evaluate, since any single
'run' of a simulation will have one of many possible outcomes. This sort of simulation is
often used as a component in demographic or ecological simulations where many of the
components are modelled using statistical criteria. They are especially useful where much
of the data upon which they are based has been amenable to statistical analysis, or where
the local behaviour of many systems is only understood in statistical terms, or where it is
convenient to model many of the components using statistical or probabilistic models,
which is desirable much of the time, even if it is not in theory necessary. It is, for example
much easier to model rainfall or warfare with a simple probabilistic model (sampling from
an empirical distribution), rather than going to the trouble of an elaborate model which is
itself extremely complex, simply because we want to estimate the impact of floods or the
loss of male population due to warfare as an independent context for our central problem.

Deterministic simulations are those where one solution set exists for a given input
situation. It primary use is extrapolate and evaluate outcomes given hypothetical inputs, or
to examine the interaction of a number of interdependent deterministic models. Depending
of the form of the form of the model used, it may not be by some definitions a simulation
proper, but I am including any animation of models within this category.

Polyvalued simulations have more than one outcome for a given starting point, but
probability plays no identifiable part in the multiplicity of outcomes (either due to lack of
knowledge or because we are not concerned with probability); there are simply many
outcomes, representing operations which are not functions. With this type of simulation
we are usually concerned with the set of solutions as a whole. This kind of simulation
could be used to explore all the possible marriages that might exist in a specific population,
and the different structural outcomes of each marriage. This allows us to at least determine
the boundaries of a system. It is useful in situations where a number of different rules
could apply in a given context.

What can we gain from using computer simulations in our research? Simulation is
appropriate where we can reasonably model the circumstances and context of some
complex behaviour, and want to explore and evaluate models of that behaviour. For
example, we are sometimes concerned with the plausibility of some practice of some stated
rules or preferences, say a preference for marriage to FB{SD}. One of the ways we can
explore a system of this sort is to examine some model under various known
circumstances to compare our own data to. In the above case we can model a population
demographically, state some rules, preferences, and conditions for marriage, and examine
some of the outcomes of applying these to the model population. It is important when
using simulation as a part of analysis to avoid two errors posed by Dyke (1981:202-3).
His methodologicalerror refers to the process of elaboration of simulations to improve
their 'realness' to the point that it is difficult to ascertain the impact any of the simulation
elements. His heuristicerror refers to concluding that 'life' mimics the simulation. The fact
that a given simulation might conform very closely to observed situations does not argue
that these operate on the same principles, only that there is some degree of logical
similarity between the processes in the simulation and the processes of the simulated
system (Dyke 1981:203).

Figure 2. Distributed simulation model.

Qualitative Simulations

Often in social anthropology we have data which is impossible to quantify or sample,
where we cannot give precise counts or parameters, or even meaningful probabilities.
Because of the nature of collecting ethnographic data, data is recorded as it arrives, and
only contextual information can be systematically collected in a form that could represent a
proper sample from which a probability could be estimated. However these qualitative
collections are the essence of ethnographic collection. They apparent serve most of the
needs of social anthropologists, and we do not tire of attempting to analyse them. Most
simulation techniques are based on numerical and/or statistical criteria, and make little
sense in analysis of marriage rules, sections, totemism or dreams.

It is possible to simulate using only qualitative objects and structures, although the use and
evaluation of these simulations are somewhat different, and in many ways more complex
than quantitative simulations, especially with respect to validation. In a qualitative
simulation we have at best categories as parameters (although these may be expressed
based on a probability), and the simulation focuses on the relationships between categories
and objects within the simulation; where the structure is as important, or more so, than the
content.

There are two basic approaches for qualitative simulations. The first is fairly conventional,
which simply consists of transforming an input structure into an output structure. This
corresponds to the usual design of a quantitative simulation. The second approach is by
resolution. With resolution we are simulating a system of rules and conditions, and using
the simulation to answer specific questions by resolving whether, given the information in
the simulation, a specific situation can occur. In the former, we alter the input structure to
generate different output structures, and evaluate these output structures. With resolution,
we hypothesise different possible output structures, and the simulation tells us whether
they are possible or not within the model of the simulation.